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Data, Engineering and Applications

Volume 1

  • Book
  • © 2019

Overview

Editors:
  1. Rajesh Kumar Shukla

    1. Department of Computer Science and Engineering, Sagar Institute of Research & Technology (SIRT), Bhopal, India

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  2. Jitendra Agrawal

    1. School of Information Technology, Rajiv Gandhi Technical University, Bhopal, India

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  3. Sanjeev Sharma

    1. School of Information Technology, Rajiv Gandhi Technological University, Bhopal, India

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  4. Geetam Singh Tomer

    1. THDC Institute of Hydropower Engineering and Technology, Tehri, India

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  • Explores challenges in Big Data in the diversified field of engineering and the sciences
  • Covers the varied applications of Big Data
  • Presents a compilation of current trends, technologies, and challenges in connection with Big Data

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Table of contents (17 chapters)

  1. Front Matter

    Pages i-viii
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  2. On Data Mining and Social Networking

    1. Front Matter

      Pages 1-1
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    2. A Review of Recommender System and Related Dimensions

      • Taushif Anwar, V. Uma
      Pages 3-10

    3. Collaborative Filtering Techniques in Recommendation Systems

      • Sandeep K. Raghuwanshi, R. K. Pateriya
      Pages 11-21

    4. Predicting Users’ Interest Through ELM-Based Collaborative Filtering

      • Shweta Tyagi, Pratibha Yadav, Moni Arora, Pooja Vashisth
      Pages 23-33

    5. Application of Community Detection Technique in Text Mining

      • Shashank Dubey, Abhishek Tiwari, Jitendra Agrawal
      Pages 35-46

    6. Sentiment Analysis on WhatsApp Group Chat Using R

      • Sunil Joshi
      Pages 47-55

    7. A Recent Survey on Information-Hiding Techniques

      • Jayant Shukla, Madhu Shandilya
      Pages 57-70

    8. Investigation of Feature Selection Techniques on Performance of Automatic Text Categorization

      • Dilip Singh Sisodia, Ankit Shukla
      Pages 71-82

    9. Identification and Analysis of Future User Interactions Using Some Link Prediction Methods in Social Networks

      • Krishna Das, Smriti Kumar Sinha
      Pages 83-94

    10. Sentiment Prediction of Facebook Status Updates of Youngsters

      • Swarnangini Sinha, Kanak Saxena, Nisheeth Joshi
      Pages 95-105

  3. On Machine Learning

    1. Front Matter

      Pages 107-107
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    2. Logistic Regression for the Diagnosis of Cervical Cancer

      • Siddharth Singh, Shweta Panday, Manjusha Panday, Siddharth S. Rautaray
      Pages 109-117

    3. Automatic Examination Timetable Scheduling Using Particle Swarm Optimization and Local Search Algorithm

      • Olusola Abayomi-Alli, Adebayo Abayomi-Alli, Sanjay Misra, Robertas Damasevicius, Rytis Maskeliunas
      Pages 119-130

    4. Personality Trait Identification for Written Texts Using MLNB

      • S. Arjaria, A. Shrivastav, A. S. Rathore, Vipin Tiwari
      Pages 131-137

    5. Deep Neural Network Compression via Knowledge Distillation for Embedded Vision Applications

      • Bhavesh Jaiswal, Nagendra P. Gajjar
      Pages 139-148

    6. An Elective Course Decision Support System Using Decision Tree and Fuzzy Logic

      • Sushmita Subramani, Sujitha Jose, Tanisha Rajesh Baadkar, Srinivasa Murthy
      Pages 149-157

    7. An Appearance-Based Gender Classification Using Radon Features

      • Ratinder Kaur Sangha, Preeti Rai
      Pages 159-169

    8. An Automatic Text Summarization on Naive Bayes Classifier Using Latent Semantic Analysis

      • Chintan Shah, Anjali Jivani
      Pages 171-180

    9. Preserving Patient Records with Biometrics Identification in e-Health Systems

      • Ambrose A. Azeta, Nicholas A. Omoregbe, Sanjay Misra, Da-Omiete A. Iboroma, E. O. Igbekele, Deborah O. Fatinikun et al.
      Pages 181-191
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Keywords

About this book

This book presents a compilation of current trends, technologies, and challenges in connection with Big Data. Many fields of science and engineering are data-driven, or generate huge amounts of data that are ripe for the picking. There are now more sources of data than ever before, and more means of capturing data. At the same time, the sheer volume and complexity of the data have sparked new developments, where many Big Data problems require new solutions. 
Given its scope, the book offers a valuable reference guide for all graduate students, researchers, and scientists interested in exploring the potential of Big Data applications. 


Editors and Affiliations

  • Department of Computer Science and Engineering, Sagar Institute of Research & Technology (SIRT), Bhopal, India

    Rajesh Kumar Shukla

  • School of Information Technology, Rajiv Gandhi Technical University, Bhopal, India

    Jitendra Agrawal

  • School of Information Technology, Rajiv Gandhi Technological University, Bhopal, India

    Sanjeev Sharma

  • THDC Institute of Hydropower Engineering and Technology, Tehri, India

    Geetam Singh Tomer

About the editors

Dr Rajesh K Shukla is a Professor and Head of the Department of Computer Science and Engineering, SIRT, Bhopal, India. With more than 20 years of teaching and research experience he has authored 8 books and has published/presented has more than 40 papers in international journals and conferences. Dr Shukla received an ISTE U.P. Government National Award in 2015 and various prestigious awards from the Computer Society of India. His research interests include recommendation systems and machine learning. He is fellow of IETE, a senior member of IEEE, a life member of ISTE, ISCA, and a member of ACM and IE(I).  


Dr Jitendra Agrawal is a member of the faculty at the Department of Computer Science & Engineering, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India. His research interests include data mining and computational intelligence. He has authored 02 books and published more than 60 papers in international journals and conferences. Dr Agrawal is a senior member of IEEE, life member of CSI, ISTE and member of IAENG. He has served as part of the program committees for several international conferences organised in countries such as the USA, India, New Zealand, Korea, Indonesia and Thailand. 


Dr Sanjeev Sharma is a Professor and Head of the School of Information Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, MP, India. He has over 29 years of teaching and research experience and received the World Education Congress Best Teacher Award in Information Technology. His research interests include mobile computing, ad-hoc networks, image processing and information security. He has edited proceedings of several national and international conferences and published more than 150 research papers in reputed journals. He is a member of IEEE, CSI, ISTE and IAENG.
 
Dr G S Tomer is the Director of THDC Institute of Hydropower Engineering and Technology (Government of Uttarakhand), Tehri, India. He received the International Plato award for Educational Achievements in 2009. He completed his doctorate in Electronics Engineering from RGPV Bhopal and postdoctorate from the University of Kent, United Kingdom. 
Dr Tomar has more than 30 years of teaching and research experience and has published over 200 research papers in reputed journals, as well as 11 books and 7 book chapters. He is a senior member of IEEE, ACM and IACSIT, a fellow of IETE and IE(I), and a member of CSI and ISTE. He has also edited the proceedings of more than 20 IEEE conferences and has been the general chair of over 30 Conferences.

Bibliographic Information

  • Book Title: Data, Engineering and Applications

  • Book Subtitle: Volume 1

  • Editors: Rajesh Kumar Shukla, Jitendra Agrawal, Sanjeev Sharma, Geetam Singh Tomer

  • DOI: https://doi.org/10.1007/978-981-13-6347-4

  • Publisher: Springer Singapore

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer Nature Singapore Pte Ltd. 2019

  • Hardcover ISBN: 978-981-13-6346-7Published: 27 March 2019

  • Softcover ISBN: 978-981-13-6349-8Published: 02 October 2020

  • eBook ISBN: 978-981-13-6347-4Published: 18 March 2019

  • Edition Number: 1

  • Number of Pages: VIII, 191

  • Number of Illustrations: 29 b/w illustrations, 60 illustrations in colour

  • Topics: Big Data, Data Mining and Knowledge Discovery, Artificial Intelligence

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